Random forest machine learning Introduction {https://t.co/9M1wNXjfse} #rstats #DataScience
— R-bloggers (@Rbloggers) February 3, 2022
11 New Books added to Big Book of R {https://t.co/zlBZjahGXb} #rstats #DataScience
— R-bloggers (@Rbloggers) February 6, 2022
Introduction to missing data (NAs) in R {https://t.co/IqsonIzhvJ} #rstats #DataScience
— R-bloggers (@Rbloggers) February 2, 2022
A new R package for Business Analytics… radiant {https://t.co/EBSvGRTpje} #rstats #DataScience
— R-bloggers (@Rbloggers) February 1, 2022
Statistical Hypothesis Testing-A Step by Step Guide {https://t.co/L30ly2gpay} #rstats #DataScience
— R-bloggers (@Rbloggers) February 6, 2022
R Markdown Lesser-Known Tips & Tricks #1: Working in the RStudio IDE {https://t.co/TZX2SrY39s} #rstats #DataScience
— R-bloggers (@Rbloggers) February 1, 2022
Teaching a Biomedical Data Science Course Using RStudio Cloud {https://t.co/ZKDXZwmmNK} #rstats #DataScience
— R-bloggers (@Rbloggers) February 6, 2022
Decision tree regression and Classification {https://t.co/QIaGtrER46} #rstats #DataScience
— R-bloggers (@Rbloggers) February 5, 2022
Guidelines for writing good R code {https://t.co/zLzieagnzC} #rstats #DataScience
— R-bloggers (@Rbloggers) February 2, 2022
Creating, optimizing and synching R shiny apps using ConnectR {https://t.co/D7DHGPzaa7} #rstats #DataScience
— R-bloggers (@Rbloggers) February 5, 2022
RStudio Cloud – How to Get Started For Free {https://t.co/Uwya8aLnsc} #rstats #DataScience
— R-bloggers (@Rbloggers) February 1, 2022
Introduction to missing data (NAs) in R {https://t.co/Zt8V7OVCrH} #rstats #DataScience
— R-bloggers (@Rbloggers) February 3, 2022
Guidelines for writing good R code {https://t.co/ej32NSAHtm} #rstats #DataScience
— R-bloggers (@Rbloggers) February 2, 2022
Finally understanding what “statistical significance” and p-values mean: A simple example (w {https://t.co/MsbbkgSlDk} #rstats #DataScience
— R-bloggers (@Rbloggers) January 9, 2022
How to Develop an R Shiny Dashboard In 10 Minutes or Less {https://t.co/DIY459wFAo} #rstats #DataScience
— R-bloggers (@Rbloggers) January 25, 2022
Time Series Forecasting Lab (Part 1) – Introduction to Feature Engineering {https://t.co/lNWsuEBa5Q} #rstats #DataScience
— R-bloggers (@Rbloggers) January 22, 2022
Time Series Forecasting Lab (Part 3) – Machine Learning with Workflows {https://t.co/QHlBMs8iGt} #rstats #DataScience
— R-bloggers (@Rbloggers) January 22, 2022
Create Customized Shiny Dashboards: Without Knowing CSS! {https://t.co/zICJnt6Pkl} #rstats #DataScience
— R-bloggers (@Rbloggers) January 31, 2022
Tips for building a Twitter bot with R and Github Actions {https://t.co/FTrKW1S0zL} #rstats #DataScience
— R-bloggers (@Rbloggers) January 16, 2022
Random forest machine learning Introduction {https://t.co/9M1wNXjfse} #rstats #DataScience
— R-bloggers (@Rbloggers) February 3, 2022
11 New Books added to Big Book of R {https://t.co/zlBZjahGXb} #rstats #DataScience
— R-bloggers (@Rbloggers) February 6, 2022
Introduction to missing data (NAs) in R {https://t.co/IqsonIzhvJ} #rstats #DataScience
— R-bloggers (@Rbloggers) February 2, 2022
How To Connect R Shiny to Postgres Database – The Definite Guide {https://t.co/RXkj93s8IF} #rstats #DataScience
— R-bloggers (@Rbloggers) January 11, 2022
A new R package for Business Analytics… radiant {https://t.co/EBSvGRTpje} #rstats #DataScience
— R-bloggers (@Rbloggers) February 1, 2022
How to join tables in R {https://t.co/axTchdaZs2} #rstats #DataScience
— R-bloggers (@Rbloggers) January 26, 2022
---
title: "RBloggers Top Tweets"
output:
flexdashboard::flex_dashboard:
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(httr)
library(lubridate)
library(jsonlite)
library(purrr)
rbloggers <- fromJSON("data/rbloggers.json")
get_tweet_embed <- function(user, status_id) {
url <-
stringr::str_glue(
"https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false"
)
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Column {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Top Tweets - 7 days {.tweet-wall}
```{r}
rblog_7 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 7, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_7_html <-
map2_chr(rblog_7$screen_name, rblog_7$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_7_html}"))
```
### Top Tweets - 30 days {.tweet-wall}
```{r}
rblog_30 <- rbloggers %>%
mutate(created_at = as_date(created_at)) %>%
filter(created_at %within% interval(start = today() - 30, end = today())) %>%
slice_max(favorite_count + retweet_count, n = 12)
rblog_30_html <-
map2_chr(rblog_30$screen_name, rblog_30$status_id, get_tweet_embed)
shiny::HTML(stringr::str_glue("{rblog_30_html}"))
```